Independent transcriptional patterns reveal biological processes associated with disease-free survival in early colorectal cancer.

BACKGROUND: Bulk transcriptional profiles of early colorectal cancer (CRC) can fail to detect biological processes associated with disease-free survival (DFS) if the transcriptional patterns are subtle and/or obscured by other processes' patterns. Consensus-independent component analysis (c-ICA) can dissect such transcriptomes into statistically independent transcriptional components (TCs), capturing both pronounced and subtle biological processes. METHODS: In this study we (1) integrated transcriptomes (n = 4228) from multiple early CRC studies, (2) performed c-ICA to define the TC landscape within this integrated data set, 3) determined the biological processes captured by these TCs, (4) performed Cox regression to identify DFS-associated TCs, (5) performed random survival forest (RSF) analyses with activity of DFS-associated TCs as classifiers to identify subgroups of patients, and 6) performed a sensitivity analysis to determine the robustness of our results RESULTS: We identify 191 TCs, 43 of which are associated with DFS, revealing transcriptional diversity among DFS-associated biological processes. A prominent example is the epithelial-mesenchymal transition (EMT), for which we identify an association with nine independent DFS-associated TCs, each with coordinated upregulation or downregulation of various sets of genes. CONCLUSIONS: This finding indicates that early CRC may have nine distinct routes to achieve EMT, each requiring a specific peri-operative treatment strategy. Finally, we stratify patients into DFS patient subgroups with distinct transcriptional patterns associated with stage 2 and stage 3 CRC.

While treatments for patients with colorectal cancer have improved, many patients (around 30-50%) have cancers that will eventually relapse and these patients will die due to their disease. Researchers have been studying the genes involved in colorectal cancer to help us understand why some cancers might relapse. However, current methods to do this may miss subtle or hidden patterns in the gene activity related to cancer relapse. To deal with this, we used a special method called consensus-independent component analysis (c-ICA) to dig more deeply into the activity of genes. This helped us to uncover some potential biological processes underpinning colorectal cancer relapse, which ultimately could help researchers to identify better treatments for patients with colorectal cancer.

LOXL2-mediated chromatin compaction is required to maintain the oncogenic properties of triple-negative breast cancer cells.

Oxidation of histone H3 at lysine 4 (H3K4ox) is catalyzed by lysyl oxidase homolog 2 (LOXL2). This histone modification is enriched in heterochromatin in triple-negative breast cancer (TNBC) cells and has been linked to the maintenance of compacted chromatin. However, the molecular mechanism underlying this maintenance is still unknown. Here, we show that LOXL2 interacts with RuvB-Like 1 (RUVBL1), RuvB-Like 2 (RUVBL2), Actin-like protein 6A (ACTL6A), and DNA methyltransferase 1associated protein 1 (DMAP1), a complex involved in the incorporation of the histone variant H2A.Z. Our experiments indicate that this interaction and the active form of RUVBL2 are required to maintain LOXL2-dependent chromatin compaction. Genome-wide experiments showed that H2A.Z, RUVBL2, and H3K4ox colocalize in heterochromatin regions. In the absence of LOXL2 or RUVBL2, global levels of the heterochromatin histone mark H3K9me3 were strongly reduced, and the ATAC-seq signal in the H3K9me3 regions was increased. Finally, we observed that the interplay between these series of events is required to maintain H3K4ox-enriched heterochromatin regions, which in turn is key for maintaining the oncogenic properties of the TNBC cell line tested (MDA-MB-231).

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Association of KRAS G12C Status with Age at Onset of Metastatic Colorectal Cancer.

The association of age at the onset of CRC and the prevalence of a KRAS G12C mutation is unclear. A retrospective, multicenter study evaluating metastatic CRC patients from January 2019 to July 2023, treated at the Oncoclinicas units and tested for tissue based KRAS/NRAS and BRAF mutations in a centralized genomics lab. A mismatch repair (MMR) status was retrieved from different labs and electronic medical records, as were patient demographics (age, gender) and tumor sidedness. The chi-square test was used to examine the association between clinical and molecular variables, with p value < 0.05 being statistically significant. A total of 858 cases were included. The median age was 63.7 years (range 22-95) and 17.4% were less than 50 years old at the diagnosis of metastatic CRC. Male patients represented 50.3% of the population. The sidedness distribution was as follows: left side 59.2%, right side 36.8% and not specified 4%. The prevalence of the KRAS mutation was 49.4% and the NRAS mutation was 3.9%. Among KRAS mutated tumors, the most common variants were G12V (27.6%) and G12D (23.5%), while KRAS G12C was less frequent (6.4%), which represented 3.1% of the overall population. The BRAF mutant cases were 7.3% and most commonly V600E. Only five (<1%) non-V600E mutations were detected. MSI-high or dMMR was present in 14 cases (1.6%). In the age-stratified analysis, left-sidedness (p < 0.001) and a KRAS G12C mutation (p = 0.046) were associated with a younger age (<50 years). In the sidedness-stratified analysis, a BRAF mutation (p = 0.001) and MSI-high/dMMR status (p = 0.009) were more common in right-sided tumors. Our data suggest that KRAS G12C mutations are more frequent in early-onset metastatic CRC. To the best of our knowledge, this is the largest cohort in the Latin American population with metastatic CRC reporting RAS, BRAF and MSI/MMR status.

Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.

Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Sagreiya in this issue.

Precision medicine for urothelial carcinoma: An international perspective.

The treatment landscape of urothelial cancers has evolved in the last decade with the approval of chemotherapy, immune checkpoint inhibitors, targeted therapies, and antibody drug conjugates. Although improvements in response and survival have been achieved with these strategies, in some scenarios their benefit is still questionable. Current efforts to identify prognostic and predictive biomarkers are crucial for better patient selection and treatment outcomes. In this paper we will review the most promising biomarkers under investigation, such as molecular classifiers, genomic alterations, programmed cell death ligand 1 expression, tumor mutational burden, circulating tumor DNA, urinary biomarkers among others, for muscle invasive bladder cancer and metastatic urothelial cancers. Deeper understanding of these biomarkers will aid clinical decision-making and help tailor treatment strategies.

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. SIGNIFICANCE: The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.

Electric hand warmer versus observation to avoid discomfort during scalp cooling for chemotherapy-induced alopecia prevention: a randomized study.

Chemotherapy-induced alopecia (CIA) is a challenge in the management of cancer patients. Scalp cooling (SC) leads to reduction in CIA, however it is associated with significant adverse events, leading to 3-13% discontinuation rates. This pilot study evaluated the role of Electric Hand Warmers (EHW) on thermal (TC), sensorial (SCo) and general comfort (GC) in patients with breast cancer (BC) undergoing chemotherapy and SC to reduce CIA. Patients were randomly assigned to EHW use or observation. TC, SCo and GC were evaluated after each chemotherapy infusion. Favorable outcomes in both TC and SCo defined a positive result on GC. We analysed the impact of age, alopecia, chemotherapy regimen and EHW use in the different comfort scales using a Logistic Regression (LR) model. Forty women with early breast cancer were randomly assigned to EHW (n = 20) or observation (n = 20) during neo(adjuvant) chemotherapy. Median age was 53 years. In the EHW arm, favorable thermal response was reported by 79% versus 50% in the control arm (odds ratio [OR] 3.79, p < 0.001). SCo was satisfactory in 82% in the EHW arm versus 74% in the control arm (OR 1.62, p = 0.1). Overall, 73% in the EHW arm had favorable GC versus 44% in the control arm (OR 3.4, p < 0.001). Age, alopecia, and chemotherapy regimen did not impact on comfort measures. Conclusion: Our study suggests that the use of an EHW has a consistent favorable impact on TC and GC of BC patients under SC technology to prevent CIA.

Cost Evaluation Analysis of Genetic Testing and Tailored Adjuvant Imatinib in Patients With Resected High-Risk GI Stromal Tumors: The Brazilian Perspective.

PURPOSE: Mutations of the KIT gene are the molecular hallmark of most GI stromal tumors (GISTs). Imatinib has revolutionized GIST treatment. Adjuvant imatinib for 3 years is the standard of care for high-risk resected GIST. However, the GIST molecular biologic profile has found different responses to this approach. Despite this, genetic testing at diagnosis is not a routine and empirical adjuvant imatinib remains the rule. Barriers to genetic profiling include concerns about the cost and utility of testing. This analysis aims to determine whether targeted genetic testing reduces costs as an ancillary tool for a limited-resource scenario instead of adjuvant empirical imatinib in patients with resected high-risk GIST. METHODS: The cost evaluation analysis of molecular testing for GIST was based on the Cost of Preventing an Event (COPE), considering the Number Needed to Treat and the costs of each test compared with the cost of 3-year empirical adjuvant imatinib and real treatment costs (median number of cycles) from the public and private Brazilian Healthcare System's perspective. The analysis compared the costs of the molecular tests (broad next-generation sequencing [NGS], GS Infinity DNA/RNA assay, and targeted NGS: GS Focus GIST and the Fleury GIST Tumor DNA sequencing panel), costs of drug acquisition, considering discounts (imatinib mesylate and Glivec), and the costs of supportive care. RESULTS: In both scenarios, public and private, regardless of the use of imatinib or Glivec, tailoring adjuvant treatment reduced costs, irrespective of the number of cycles. The only exception was the combination of the broad NGS test and imatinib in the Public Healthcare System. CONCLUSION: The molecularly tailored adjuvant imatinib reduced costs considering the COPE of available NGS tests for both the public and private Brazilian health care systems.

Clinical and genomic characterisation of early-onset pancreatic cancer.

BACKGROUND: The incidence of early-onset pancreatic cancer (EOPC) has risen dramatically in recent years. We aimed to characterise the clinical and genomic features of EOPC and evaluate their therapeutic implications. METHODS: We performed a comparative, single-centre, retrospective analysis of clinical, germline, and genomic features in EOPC (≤50 years) patients and compared them with a control group of average-onset pancreatic cancer patients (AOPC, ≥70 years). Key molecular findings were compared with an external, publicly available cohort. RESULTS: We reviewed 336 patients who met all inclusion criteria (EOPC N = 139, AOPC N = 197). EOPC was associated with smoking status, lower prevalence of diabetes, better performance status, higher CA19.9 levels, and higher albumin levels at diagnosis. After adjustment for baseline covariates, we observed no differences in overall survival (OS). Age was associated with an increase in the incidence of KRASMUT both in our cohort and the validation cohort. EOPC were enriched in potentially actionable alterations according to ESCAT tiers I-IIIA when compared with AOPC in discovery and validation cohorts (19% versus 14% and 14% versus 8%, respectively). In the first-line metastatic setting, EOPC had a longer progression-free survival (hazard ratio [HR] 0.61, 95% confidence interval (CI) 0.43-0.87) and OS (HR 0.65, 95% CI 0.45-0.95), although there were no differences in response rate. After adjusting for the number of treatment lines, EOPC patients who did receive targeted therapies exhibited longer OS compared with EOPC who did not (HR 0.34, 95% CI 0.12-0.93). CONCLUSIONS: EOPC patients have improved outcomes in the metastatic setting when compared to AOPC and are enriched for targetable alterations that open opportunities for precision oncology-based approaches.

Precision medicine in Thoracic Oncology: understanding disparities to tackle inequities in access.

INTRODUCTION: Precision medicine is defined as personalized interventions fitted to patients' or tumors' characteristics. Patients diagnosed with different neoplasms have benefited from a personalized therapeutic approach in terms of response and survival. However, several challenges must be addressed for precision oncology to become a global reality. Access to genomic testing that allows biomarker identification is a main issue. AREAS COVERED: A nonsystematic literature review about inequities in access to molecular genetic testing, focusing on lung cancer as the prominent example, was performed by a group of expert clinical oncologists. EXPERT OPINION: Access to molecular tests and their matched treatments differ between regions of the world and even among diverse populations in the same country. Socioeconomic characteristics are often strongly correlated with this disparity. Furthermore, although the cost is a determinant factor for inequality, other issues have been recognized. Advances in the education of healthcare professionals, patient advocacy initiatives, building local laboratory workstreams, and promoting favorable regulatory environment are vital factors in promoting equal access.

Early-Stage Breast Cancer Detection in Breast Milk.

Breast cancer occurring during pregnancy (PrBC) and postpartum (PPBC) is usually diagnosed at more advanced stages compared with other breast cancer, worsening its prognosis. PPBC is particularly aggressive, with increased metastatic risk and mortality. Thus, effective screening methods to detect early PrBC and PPBC are needed. We report for the first time that cell-free tumor DNA (ctDNA) is present in breast milk (BM) collected from patients with breast cancer. Analysis of ctDNA from BM detects tumor variants in 87% of the cases by droplet digital PCR, while variants remain undetected in 92% of matched plasma samples. Retrospective next-generation sequencing analysis in BM ctDNA recapitulates tumor variants, with an overall clinical sensitivity of 71.4% and specificity of 100%. In two cases, ctDNA was detectable in BM collected 18 and 6 months prior to standard diagnosis. Our results open up the potential use of BM as a new source for liquid biopsy for PPBC detection. SIGNIFICANCE: For the first time, we show that BM obtained from patients with breast cancer carries ctDNA, surpassing plasma-based liquid biopsy for detection and molecular profiling of early-stage breast cancer, even prior to diagnosis by image. See related commentary by Cunningham and Turner, p. 2125. This article is featured in Selected Articles from This Issue, p. 2109.

Challenges and opportunities in building a health economic framework for personalized medicine in oncology.

Personalized medicine has allowed for knowledge at an individual level for several diseases and this has led to improvements in prevention and treatment of various types of neoplasms. Despite the greater availability of tests, the costs of genomic testing and targeted therapies are still high for most patients, especially in low- and middle-income countries. Although value frameworks and health technology assessment are fundamental to allow decision-making by policymakers, there are several concerns in terms of personalized medicine pharmacoeconomics. A global effort may improve these tools in order to allow access to personalized medicine for an increasing number of patients with cancer.

A pan-cancer clinical platform to predict immunotherapy outcomes and prioritize immuno-oncology combinations in early-phase trials.

BACKGROUND: Immunotherapy is effective, but current biomarkers for patient selection have proven modest sensitivity. Here, we developed VIGex, an optimized gene signature based on the expression level of 12 genes involved in immune response with RNA sequencing. METHODS: We implemented VIGex using the nCounter platform (Nanostring) on a large clinical cohort encompassing 909 tumor samples across 45 tumor types. VIGex was developed as a continuous variable, with cutoffs selected to detect three main categories (hot, intermediate-cold and cold) based on the different inflammatory status of the tumor microenvironment. FINDINGS: Hot tumors had the highest VIGex scores and exhibited an increased abundance of tumor-infiltrating lymphocytes as compared with the intermediate-cold and cold. VIGex scores varied depending on tumor origin and anatomic site of metastases, with liver metastases showing an immunosuppressive tumor microenvironment. The predictive power of VIGex-Hot was observed in a cohort of 98 refractory solid tumor from patients treated in early-phase immunotherapy trials and its clinical performance was confirmed through an extensive metanalysis across 13 clinically annotated gene expression datasets from 877 patients treated with immunotherapy agents. Last, we generated a pan-cancer biomarker platform that integrates VIGex categories with the expression levels of immunotherapy targets under development in early-phase clinical trials. CONCLUSIONS: Our results support the clinical utility of VIGex as a tool to aid clinicians for patient selection and personalized immunotherapy interventions. FUNDING: BBVA Foundation; 202-2021 Division of Medical Oncology and Hematology Fellowship award; Princess Margaret Cancer Center.

Precision cancer medicine: Concepts, current practice, and future developments.

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Expression of TILs and Patterns of Gene Expression from Paired Samples of Malignant Pleural Mesothelioma (MPM) Patients.

MPM is an aggressive disease with an immunosuppressive tumor microenvironment, and interest in exploring immunotherapy in this disease has been increasing. In the first line of treatment, the combination of nivolumab and ipilimumab demonstrated an improvement in survival over chemotherapy. The presence of TILs has been recognized as a marker of antitumor immune response to chemotherapy in solid tumors. The aim of our study is to identify the effect of treatment on immune cells and the immune gene profile in MPM. We investigated the changes in expression of TILs in 10 human MPM paired tumor tissues using immunohistochemistry and gene expression analysis from paired untreated and treated samples. In this small series, we demonstrated that during the evolution of disease without any treatment there was an increase in the inflammatory component in tumor samples. After systemic treatment there was a decrease in the number of TILs. We observed that after systemic treatment or disease progression immune gene signatures were suppressed. Our integrated analysis of paired samples with immune profile and genomic changes on MPM suggested that during the evolution of the disease the immune system tends to switch, turning off with treatment.

Complete Response to Immunotherapy in a Patient with MUTYH-Associated Polyposis and Gastric Cancer: A Case Report.

MUTYH-associated polyposis syndrome is an uncommon, autosomal recessive colorectal polyposis syndrome caused by biallelic inactivation of MUTYH. Most patients present with multiple colorectal polyps. However, other primary tumor sites have been described as less frequent. In this report, we describe the case of a young patient with a germline biallelic pathogenic MUTYH mutation with three different primary tumors. We focused on a metastatic gastric adenocarcinoma that presented with complete bowel obstruction secondary to extensive peritoneal carcinomatosis and achieved complete response upon treatment with immunotherapy. The patient's tumor presented with a high tumor mutational burden and a 100% combined positive score, which certainly contributed to the complete response to immunotherapy. To date, no studies have described the association of MUTYH-related tumors with high PD-L1 expression, but we hypothesized that it may be linked to the increased antigenicity of these cancers.

Comprehensive evaluation of surrogate endpoints to predict overall survival in trials with PD1/PD-L1 immune checkpoint inhibitors plus chemotherapy.

BACKGROUND: PD1/PD-L1 immune checkpoint inhibitors (ICI) have revolutionized cancer treatment. Although there is controversy about the accuracy of surrogate endpoints in the ICI setting to predict overall survival (OS), these endpoints are commonly used in confirmatory trials. Here we aimed to explore the validity of classical and novel surrogate endpoints in randomised controlled trials (RCT) that combine ICI plus chemotherapy (CT) in the first-line setting. MATERIAL AND METHODS: A systematic review was conducted to identify RCT investigating anti-PD1/PD-L1 drugs plus CT versus CT alone. We performed (i) arm-level analysis to evaluate predictors of median OS (mOS) and (ii) comparison-level analysis for OS hazard ratio (HR) estimations. Linear regression models weighted by trial size were fitted and adjusted R2 values were reported. RESULTS: Thirty-nine RCTs involving 22,341 patients met the inclusion criteria (17 non-small cell lung, 9 gastroesophageal and 13 in other cancers) with ten different ICI under study. Overall, ICI plus CT improved OS (HR = 0.76; 95%CI: 0.73-0.80). In the arm-level analysis, the best mOS prediction was obtained with a new endpoint that combines median duration of response and ORR (mDoR-ORR) and with median PFS (R2 = 0.5 both). In the comparison-level analysis, PFS HR showed a moderate association with OS HR (R2 = 0.52). Early OS read-outs were highly associated with final OS outcomes (R2 = 0.80). CONCLUSIONS: The association between surrogate endpoints and OS in first-line RCT combining anti-PD1/PD-L1 and CT is moderate-low. Early OS read-outs showed a good association with final OS HR while the mDOR-ORR endpoint could help to better design confirmatory trials after single-arm phase II trials.